System simulation without detailed prior knowledge or data of the system is a complex challenge. In this paper we present an approach to automatically generate a model on the fly in a symbiotic way. Basically the data...
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This paper proposes some new evolutionary and classification methods for the delineation of local labor markets (LLMs) in areas where there are a large number of small localities with little labor interaction. The evo...
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This paper proposes some new evolutionary and classification methods for the delineation of local labor markets (LLMs) in areas where there are a large number of small localities with little labor interaction. The evolutionary methods presented here, based on previous works of Flrez-Revuelta et al. (Int J Autom Comput 5:10-21, 2008a;PPSN X, LNCS 5199:1011-1020, 2008b) and Martinez-Bernabeu et al. (Expert Syst Appl 39:6754-6766, 2012), decrease their computational times (up to a 99 %) without deteriorating the quality and robustness of the solutions. Also, in this work we avoid geographical contiguity constraints because such restrictions might reduce the realism of the process. Another contribution of this paper is related to the location of new services-hospitals, schools, employment centers, etc.-taking into account the labor mobility patterns. In this context, we present a cluster partitioning of k-means procedure, which captures the common aspects of all the potential solutions of these evolutionary algorithms and allows us to rank the LLMs foci, understood as the main centers of activity of the markets. The performance of the algorithms is analyzed through a real commuting dataset of the region of Aragn (Spain).
In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approa...
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In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.
The main objective of this research was to compare the results obtained from modelling irrigation water allocation decisions within a single-stage decision-making framework with the results obtained within a multi-sta...
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The main objective of this research was to compare the results obtained from modelling irrigation water allocation decisions within a single-stage decision-making framework with the results obtained within a multi-stage sequential decision-making framework under a full water quota and a restricted water quota. A unified irrigation decision-making framework was developed to model the impact of the interaction between water availability, irrigation area and irrigation scheduling decisions as multi-stage sequential decisions on gross margin variability. An Excel ® risk simulation model that utilises evolutionary algorithms embedded in Excel® based on the Soil Water Irrigation Planning and Energy management (SWIP-E) programming model was developed and applied to optimise irrigation water use. The model facilitates the simulation of the economic consequences resulting from changes to the key decision variables that need to be optimised through gross margin calculations for each state of nature. Risk enters the simulation model as crop yield risk through different potential crop yields in each state of nature and stochastic weather which determines irrigation management decisions. Water budget calculations were replicated to include 12 states of nature within a crop rotation system of maize and wheat. The risk simulation model was applied in Douglas, a typical location of an irrigation farm. The results showed improved risk management within a multi-stage decision-making framework as indicated by higher gross margins and reduced variability due to improved irrigation scheduling decisions under both a full and restricted water quota scenario. Close to potential yields, if not full potential yields were achieved within both decision-making frameworks. However, a significant reduction in per state irrigation water use resulted within a multi-stage decision-making framework sequentially resulting in improved gross margins. A full irrigation strategy with reduced areas was fol
In this paper a co-processor for the hardware aided decision tree induction using evolutionary approach (EFTIP) is proposed. EFTIP is used for hardware acceleration of the fitness evaluation task since this task is pr...
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In this paper a co-processor for the hardware aided decision tree induction using evolutionary approach (EFTIP) is proposed. EFTIP is used for hardware acceleration of the fitness evaluation task since this task is proven in the paper to be the execution time bottleneck. The EFTIP co-processor can significantly improve the execution time of a novel algorithm for the full decision tree induction using evolutionary approach (EFTI) when used to accelerate the fitness evaluation task. The comparison of the HW/SW EFTI implementation with the pure software implementation suggests that the proposed HW/SW architecture offers substantial DT induction time speedups for the selected benchmark datasets from the standard UCI machine learning repository database. (C) 2016 Elsevier B.V. All rights reserved.
This paper presents an evolutionary algorithm for the fixed-charge multicommodity network design problem (MCNDP), which concerns routing multiple commodities from origins to destinations by designing a network through...
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This paper presents an evolutionary algorithm for the fixed-charge multicommodity network design problem (MCNDP), which concerns routing multiple commodities from origins to destinations by designing a network through selecting arcs, with an objective of minimizing the fixed costs of the selected arcs plus the variable costs of the flows on each arc. The proposed algorithm evolves a pool of solutions using principles of scatter search, interlinked with an iterated local search as an improvement method. New cycle-based neighborhood operators are presented which enable complete or partial re-routing of multiple commodities. An efficient perturbation strategy, inspired by ejection chains, is introduced to perform local compound cycle-based moves to explore different parts of the solution space. The algorithm also allows infeasible solutions violating arc capacities while performing the "ejection cycles", and subsequently restores feasibility by systematically applying correction moves. Computational experiments on benchmark MCNDP instances show that the proposed solution method consistently produces high-quality solutions in reasonable computational times. (C) 2016 Published by Elsevier B.V.
In this paper, we propose a complete, fully automatic and efficient clinical decision support system for breast cancer malignancy grading. The estimation of the level of a cancer malignancy is important to assess the ...
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In this paper, we propose a complete, fully automatic and efficient clinical decision support system for breast cancer malignancy grading. The estimation of the level of a cancer malignancy is important to assess the degree of its progress and to elaborate a personalized therapy. Our system makes use of both Image Processing and Machine Learning techniques to perform the analysis of biopsy slides. Three different image segmentation methods (fuzzy c-means color segmentation, level set active contours technique and grey-level quantization method) are considered to extract the features used by the proposed classification system. In this classification problem, the highest malignancy grade is the most important to be detected early even though it occurs in the lowest number of cases, and hence the malignancy grading is an imbalanced classification problem. In order to overcome this difficulty, we propose the usage of an efficient ensemble classifier named EUSBoost, which combines a boosting scheme with evolutionary undersampling for producing balanced training sets for each one of the base classifiers in the final ensemble. The usage of the evolutionary approach allows us to select the most significant samples for the classifier learning step (in terms of accuracy and a new diversity term included in the fitness function), thus alleviating the problems produced by the imbalanced scenario in a guided and effective way. Experiments, carried on a large dataset collected by the authors, confirm the high efficiency of the proposed system, shows that level set active contours technique leads to an extraction of features with the highest discriminative power, and prove that EUSBoost is able to outperform state-of-the-art ensemble classifiers in a real-life imbalanced medical problem. (C) 2015 Elsevier B.V. All rights reserved.
Over the last decade, several bio-inspired algorithms have emerged for solving complex optimisation problems. Since the performance of these algorithms present a suboptimal behaviour, a tremendous amount of research h...
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Over the last decade, several bio-inspired algorithms have emerged for solving complex optimisation problems. Since the performance of these algorithms present a suboptimal behaviour, a tremendous amount of research has been devoted to find new and better optimisation methods. On the other hand, allostasis is a medical term recently coined which explains how the configuration of the internal state (IS) in different organs allows reaching stability when an unbalance condition is presented. In this paper, a novel biologically-inspired algorithm called allostatic optimisation (AO) is proposed for solving optimisation problems. In AO, individuals emulate the IS of different organs. In the approach, each individual is improved by using numerical operators based on the biological principles of the allostasis mechanism. The proposed method has been compared to other well-known optimisation algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.
In this paper a novel problem in MGs (Migrogrids) is analyzed. The problem consists in the joint optimization of the MG structure and operation, by obtaining on the one hand an optimal sizing of its elements and struc...
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In this paper a novel problem in MGs (Migrogrids) is analyzed. The problem consists in the joint optimization of the MG structure and operation, by obtaining on the one hand an optimal sizing of its elements and structural parameters, and on the other hand the scheduling for the ESS (energy storage system) in the MG. For this joint problem, a novel two-steps EA (evolutionary algorithm) is proposed. The EA operates in such a way that a first EA obtains the best MG structure, mainly the optimal values for the sizing of generators and ESS, and a second EA determines the operational part of the MG (ESS scheduling). A real scenario of variable electricity prices is considered. In this scenario, power demanded from the main grid has different prices depending on the day and the hour of the day when it is demanded. The MG considered in this paper is formed by wind and photovoltaic generators, different residential and industrial loads, as well as ESS. Moreover, four different settings with different natural resource avail abilities have been analyzed, and the results obtained show a significant cost improvement in the MG's performance. (C) 2015 Elsevier Ltd. All rights reserved.
This paper proposes a multi-objective evolutionary algorithm (MOEA)-based task scheduling approach for determining Pareto optimal solutions with simultaneous optimization of performance (P), energy (E), and temperatur...
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This paper proposes a multi-objective evolutionary algorithm (MOEA)-based task scheduling approach for determining Pareto optimal solutions with simultaneous optimization of performance (P), energy (E), and temperature (T). Our algorithm includes problem-specific solution encoding, determining the initial population of the solution space, and the genetic operators that collectively work on generating efficient solutions in fast turnaround time. Multiple schedules offer a diverse range of values for makespan, energy consumed, and peak temperature and thus present an efficient way of identifying trade-offs among the desired objectives, for a given application and machine pair. We also present a methodology for selecting one solution from the Pareto front given the user's preference. The proposed algorithm for scheduling tasks to cores achieves three-way optimization with fast turnaround time. The proposed algorithm is advantageous because it reduces both energy and temperature together rather than in isolation. We evaluate the proposed algorithm using implementation and simulation, and compare it with integer linear programming as well as with other scheduling algorithms that are energy-or thermal-aware. The time complexity of the proposed scheme is considerably better than the compared algorithms.
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